SOTAVerified

Federated Learning

Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.

This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.

Papers

Showing 50515100 of 6771 papers

TitleStatusHype
Lower Bounds and Optimal Algorithms for Personalized Federated Learning0
Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning0
Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks0
Low-Latency Federated Learning over Wireless Channels with Differential Privacy0
Low-Rank Training of Deep Neural Networks for Emerging Memory Technology0
Low Rank Training of Deep Neural Networks for Emerging Memory Technology0
LSTM-Based Distributed Conditional Generative Adversarial Network For Data-Driven 5G-Enabled Maritime UAV Communications0
LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data0
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning0
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning0
M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction0
MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments0
Blind Federated Learning at the Wireless Edge with Low-Resolution ADC and DAC0
Machine Learning for Large-Scale Optimization in 6G Wireless Networks0
Machine learning in business process management: A systematic literature review0
Machine Learning Techniques for MRI Data Processing at Expanding Scale0
Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity0
Making Batch Normalization Great in Federated Deep Learning0
MammoFL: Mammographic Breast Density Estimation using Federated Learning0
Management of Resource at the Network Edge for Federated Learning0
MANDERA: Malicious Node Detection in Federated Learning via Ranking0
Many-Objective Multi-Solution Transport0
Many-Task Federated Fine-Tuning via Unified Task Vectors0
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes0
Markov Chain Mirror Descent On Data Federation0
MarS-FL: Enabling Competitors to Collaborate in Federated Learning0
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization0
Massive MIMO for Serving Federated Learning and Non-Federated Learning Users0
MAS: Towards Resource-Efficient Federated Multiple-Task Learning0
Matching Pursuit Based Scheduling for Over-the-Air Federated Learning0
OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning0
Maverick-Aware Shapley Valuation for Client Selection in Federated Learning0
Maximizing Model Generalization for Machine Condition Monitoring with Self-Supervised Learning and Federated Learning0
Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning0
Maze Discovery using Multiple Robots via Federated Learning0
MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing0
Measure Contribution of Participants in Federated Learning0
Measuring Heterogeneity in Machine Learning with Distributed Energy Distance0
Measuring Participant Contributions in Decentralized Federated Learning0
Mechanisms that Incentivize Data Sharing in Federated Learning0
MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation0
Membership Inference Attacks on Deep Regression Models for Neuroimaging0
Memory Backdoor Attacks on Neural Networks0
MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation0
Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios0
Meta Federated Learning0
Meta-Federated Learning: A Novel Approach for Real-Time Traffic Flow Management0
Meta Federated Reinforcement Learning for Distributed Resource Allocation0
Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75Unverified
2SiloBN + SAMmIoU49.1Unverified
3SiloBNmIoU45.96Unverified
4FedSAM + SWAmIoU43.42Unverified
5FedASAM + SWAmIoU43.02Unverified
6FedAvg + SWAmIoU42.48Unverified
7FedASAMmIoU42.27Unverified
8FedSAMmIoU41.22Unverified
9FedAvgmIoU38.65Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32Unverified
2FedSAM + SWAAcc@1-1262Clients68.12Unverified
3FedAvg + SWAAcc@1-1262Clients67.52Unverified
4FedASAMAcc@1-1262Clients64.23Unverified
5FedSAMAcc@1-1262Clients63.72Unverified
6FedAvgAcc@1-1262Clients61.91Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64Unverified
2FedASAMACC@1-100Clients39.76Unverified
3FedSAM + SWAACC@1-100Clients39.51Unverified
4FedSAMACC@1-100Clients36.93Unverified
5FedAvgACC@1-100Clients36.74Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62Unverified
2FedASAMACC@1-100Clients40.81Unverified
3FedSAM + SWAACC@1-100Clients39.24Unverified
4FedAvgACC@1-100Clients38.59Unverified
5FedSAMACC@1-100Clients38.56Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72Unverified
2FedSAM + SWAACC@1-100Clients46.76Unverified
3FedASAMACC@1-100Clients46.58Unverified
4FedSAMACC@1-100Clients44.84Unverified
5FedAvgACC@1-100Clients41.27Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27Unverified
2FedASAMACC@1-100Clients47.78Unverified
3FedSAM + SWAACC@1-100Clients46.47Unverified
4FedSAMACC@1-100Clients46.05Unverified
5FedAvgACC@1-100Clients42.17Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17Unverified
2FedSAM + SWAACC@1-100Clients47.96Unverified
3FedASAMACC@1-100Clients45.61Unverified
4FedSAMACC@1-100Clients44.73Unverified
5FedAvgACC@1-100Clients40.43Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01Unverified
2FedSAM + SWAACC@1-100Clients39.3Unverified
3FedASAMACC@1-100Clients36.04Unverified
4FedSAMACC@1-100Clients31.04Unverified
5FedAvgACC@1-100Clients30.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97Unverified
2FedASAM + SWAACC@1-100Clients54.79Unverified
3FedSAM + SWAACC@1-100Clients53.67Unverified
4FedSAMACC@1-100Clients53.39Unverified
5FedAvgACC@1-100Clients50.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5Unverified
2FedSAM + SWAACC@1-100Clients54.36Unverified
3FedASAM + SWAACC@1-100Clients54.1Unverified
4FedSAMACC@1-100Clients53.97Unverified
5FedAvgACC@1-100Clients50.66Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81Unverified
2FedSAMACC@1-100Clients54.01Unverified
3FedSAM + SWAACC@1-100Clients53.9Unverified
4FedASAM + SWAACC@1-100Clients53.86Unverified
5FedAvgACC@1-100Clients49.92Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2Unverified